Business intelligence technologies — or BI technologies — are an essential part of doing business in today’s world. Known as decision support technologies, their primary purpose is allowing businesses to collect data more quickly and concisely, thus enabling crucial decision-making to take place. This drive for speed and efficiency became the catalyst for creating new and improved types of BI technologies, including data mining and text analytics, cloud data services, web analytics, predictive technologies, and near real-time monitoring. These technologies help ensure enterprises have the capacity to acquire and store large amounts of data, proactively analyze their data, and then provide customized products and services for customers.

Data mining represents a research form of business intelligence. This version of BI technology helps business owners conduct comprehensive data analysis leading to predictive models, which can better highlight specific future trends. Text analytics allow businesses to extract key phrases from answers to survey questions. These answers help companies categorize and analyze results to structure future action.

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Other types of business intelligence technologies are cloud data services and web analytics. Cloud data services refer to using the Internet as a virtual office space to share files and data on either a public or private basis. Storing and analyzing data on the cloud allows for greater computing power and capacity than might be afforded by some businesses in house. Web analytics consider data on visitor behavior to a company website, such as length of time spent on the home page, click-through rate to additional pages, and purchase frequency.

Basic business intelligence focuses on data concerning volume of products or services sold, customer demographics, and profit margins. This data collection allows businesses to create forecasts for future business trends. Predictive technology, however, offers an enhanced version combining basic BI data, data mining, and statistical analysis. The result is predictive analytics, a more complex form of BI technologies that ventures beyond “guesstimates” typically based on general forecasting.

Predictive analytics provides more concrete predictions grounded in statistics and specific outcomes. For example, forecasting in general business intelligence technologies may inform an enterprise selling sports apparel that a certain season accounts for the highest sales volume, based on past experience. The crucial difference with predictive analytics is the data would indicate customer characteristics and behaviors, their specific choices of apparel, and the type of marketing that would appeal to the majority of them.

Near real-time monitoring is one of the more significant types of business intelligence technologies and is geared toward closing the gap between data acquisition and data analysis. One example of near real-time monitoring includes using transportation ticket data to match passengers with the most appropriate flight, bus, or train. Another example is using data about emergency patients to trigger the quickest essential care by applicable medical personnel.

Any business owner, executive, or manager who desires greater success in his or her enterprise should consider incorporating business intelligence technologies to improve the quality and efficiency of the business. BI technology allows individuals to make quicker, more informed decisions based on highly accurate statistics. Businesses that can benefit from BI technologies include those in financial services, health care, manufacturing, retail, telecommunications, transportation, and utilities.

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